DTSC13-302: Statistical Learning and Regression Models

Description

The theory and practice of advanced regression techniques is the focus of this subject. Topics such as regularisation, limited dependent variable models, generalised linear models, random and mixed effects models, splines, additive models and tree-based regression will be covered. The programming language R will be used in this course.

Subject details

Type:	Undergraduate Subject
Code:	DTSC13-302
EFTSL:	0.125
Faculty:	Bond Business School
Semesters offered:	May 2023 [Standard Offering]
Credit:	10
Study areas:	Actuarial Science and Data Analytics
Subject fees:	Commencing in 2022: $3,950.00 Commencing in 2023: $4,050.00 Commencing in 2024: $4,260.00 Commencing in 2022: $5,260.00 Commencing in 2023: $5,400.00 Commencing in 2024: $5,730.00

Learning outcomes

Demonstrate knowledge of the limitations of normal linear regression models and the ability to appropriately interpret linear model outputs considering these limitations.
Evaluate and appropriately choose between applications of a variety of linear and generalised linear regression modelling structures, including regularisation, dimension reduction and sequential variable selection.
Apply non-normal dependent variable regression and classification models and identify their structural differences.
Develop prediction models utilising splines, polynomials, recursive partitioning and general additive methods.
Demonstrate ability to produce creative analytic solutions addressing a specified issue or problem.
Demonstrate ability to verbally communicate the results of a statistical learning investigation.

Enrolment requirements

Requisites:	Nil
Assumed knowledge:	Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent): ECON12-200: Linear Models and Applied Econometrics DTSC12-200: Data Science Possess demonstrable knowledge in the theory and application of simple and multiple linear regression models to the level of a unit such as ECON12-200 Linear Models and Applied Econometrics as well as basic data science concepts and techniques to the level of a unit such as DTSC12-200 Data Science
Restrictions:

Requisites:

Nil

Assumed knowledge:

Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.

Assumed Prior Learning (or equivalent):

Possess demonstrable knowledge in the theory and application of simple and multiple linear regression models to the level of a unit such as ECON12-200 Linear Models and Applied Econometrics as well as basic data science concepts and techniques to the level of a unit such as DTSC12-200 Data Science

Restrictions:

Subject outlines

Subject dates

May 2023

Standard Offering
Enrolment opens:	19/03/2023
Semester start:	15/05/2023
Subject start:	15/05/2023
Cancellation 1:	29/05/2023
Cancellation 2:	05/06/2023
Last enrolment:	28/05/2023
Withdraw - Financial:	10/06/2023
Withdraw - Academic:	01/07/2023
Teaching census:	09/06/2023

Standard Offering
Enrolment opens:	19/03/2023
Semester start:	15/05/2023
Subject start:	15/05/2023
Cancellation 1:	29/05/2023
Cancellation 2:	05/06/2023
Last enrolment:	28/05/2023
Withdraw - Financial:	10/06/2023
Withdraw - Academic:	01/07/2023
Teaching census:	09/06/2023